Molecular Modeling of Exquisitely Selective c-Met Inhibitors through 3D-QSAR and Molecular Dynamics Simulations
Bibliographic record
Abstract
c-Met has been considered as an attractive target for developing antitumor agents. The highly selective c-Met inhibitors provide invaluable opportunities for the combination with other therapies safely to achieve the optimal efficacy. In this work, a series of triazolopyrazine c-Met inhibitors with exquisitely selectivity were investigated using a combination of molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics simulation. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models were developed to reveal the structural determinants for c-Met inhibition. Both models were validated to have high reliability and predictability, and contour map analysis suggested feature requirements for different substituents on the scaffold. It is worth noting that an important hydrogen bond rich region was identified in the unique narrow channel, which is distinct from other kinases. Molecular dynamics simulations and binding free energy calculations provided further support that suitable groups in this hydrogen bond rich region made great contributions to the binding of ligands. Moreover, hydrogen bonds with residues of the narrow channel were also indicated to be essential to improve the activity and selectivity. This study will facilitate the discovery and optimization of novel c-Met inhibitors with higher activity and selectivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".